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tla-plus-expert特拉加专家

Agent Skill

tla-plus-expert 用于处理 GitHub 仓库、Issue、Pull Request 和代码协作信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要围绕仓库状态、代码变更或协作事项进行整理时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

360

周安装

15

GitHub Stars

55

下载量

120
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:tla-plus-expert(特拉加专家)
来源仓库:https://github.com/rysweet/amplihack
仓库路径:skills/tla-plus-expert
安装命令:
npx skills add https://github.com/rysweet/amplihack --skill tla-plus-expert
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/rysweet/amplihack --skill tla-plus-expert

简介

tla-plus-expert 用于处理 GitHub 仓库、Issue、Pull Request 和代码协作信息,适合在 Codex、Claude、Cursor、Gemini CLI 中围绕仓库状态或协作事项进行整理。

  • 适用于代码协作和仓库管理场景,可结合来源仓库和原始 README 核验用法。
  • 通过 npx skills add 命令从 GitHub 安装,支持主流 AI 宿主环境。
  • 安装前建议确认权限范围、维护状态,以及是否会触发联网或文件读写。
  • 适用宿主包括 Codex、Claude、Cursor、Gemini CLI,接入前应确认版本、权限和运行环境要求。

SKILL.md

name
tla-plus-expert
version
1.0.0
description
TLA+ formal specification expert for writing specs, model checking, and applying formal methods to amplihack workflows
activation_keywords
agent
amplihack:specialized:tla-plus-expert

TLA+ Expert Skill

Purpose

Provides expert-level TLA+ formal specification assistance for designing, verifying, and reasoning about concurrent and distributed systems within amplihack.

When This Skill Activates

  • User asks to write or review a TLA+ specification
  • User needs help with model checking (TLC) configuration or output interpretation
  • User wants to formally verify a protocol or workflow design
  • User asks about invariants, liveness properties, or safety properties
  • User wants to apply formal methods to amplihack components
  • User mentions PlusCal or wants to translate between PlusCal and TLA+
  • User references Lamport, Demirbas, or formal methods best practices

How It Works

This skill delegates to the tla-plus-expert agent which has deep knowledge of:

  1. TLA+ language and idioms — writing specs, operators, temporal formulas
  2. TLC model checker — configuration, trace interpretation, state space management
  3. Seven mental models (Demirbas) — abstraction, global shared memory, local guards, invariants, stepwise refinement, atomicity refinement, communication
  4. Industry case studies — 8 production uses from AWS, MongoDB, Microsoft Azure
  5. AI + TLA+ limitations — SysMoBench findings on LLM capabilities and guardrails
  6. amplihack experiment infrastructure — manifest-driven experiments, heuristic scoring, TLC validation integration

Integration with Existing Infrastructure

The amplihack repo includes a TLA+ experiment runner at crates/amplihack-eval/src/tla_prompt_experiment.rs with:

  • Manifest-driven experiment matrix (models x prompt variants x repeats)
  • 6 heuristic scoring dimensions
  • Real TLC validation support
  • Replay and live execution modes

TLA+ specs live in experiments/hive_mind/tla_prompt_language/specs/.

Usage Examples

# Write a spec for a new protocol
/tla-plus-expert Write a TLA+ spec for our consensus voting workflow

# Review an existing spec
/tla-plus-expert Review specs/SmartOrchestrator.tla for correctness

# Help with TLC output
/tla-plus-expert TLC found a counterexample in my spec, help me understand it

# Decide if TLA+ is appropriate
/tla-plus-expert Should I formally specify this retry cascade logic?

# Generate invariants
/tla-plus-expert What invariants should I check for a parallel workstream manager?

Key Resources

  • TLA+ specs: experiments/hive_mind/tla_prompt_language/specs/
  • Experiment runner: crates/amplihack-eval/src/tla_prompt_experiment.rs
  • TLC binary: /usr/local/bin/tlc (if installed)
  • Issue #3939: TLA+ integration roadmap

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Codex

35.72%
按下载量换算43

Claude

29.13%
按下载量换算35

Cursor

16.76%
按下载量换算20

Gemini CLI

9.64%
按下载量换算12

安全审计

暂无安全审计结果可展示。

权限和风险

权限需确认

当前来源未能明确判断权限范围,默认进入异常复核队列。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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